Microsoft Extends ML Framework

George Leopold

(Aleutie/Shutterstock)

The latest release of Microsoft’s machine learning framework incorporates a new API intended to help .NET developers train models for improved predictions in uses cases like image classification and speech translation.

Microsoft said the 0.6 version of its ML.NET machine learning framework released this week expands the number of data pipelines that can be used to build machine learning models. Previous versions limited the types of pipelines that could be used to train models. The new version also improves model prediction performance, the company said this week.

An earlier version of ML.NET added support for TensorFlow models with the goal of using deep learning models to improve prediction performance for uses cases such as image classification, speech to text and translations. The new version adds support for predictions derived from the Open Neural Network Exchange format, which is billed as an open platform for interchangeable AI models. ONNX is backed by Microsoft, Amazon Web Services (NASDAQ: AMZN) and Facebook (NASDAQ: FB).

Microsoft (NASDAQ: MSFT) said the latest version of its machine learning framework allows .NET developers to use ONNX models to score, or predict, the performance of trained models. The new version makes use of ONNX models trained in multiple frameworks ranging from ML.NET to TensorFlow that can be exported to ONNX. The resulting models can also be used for machine learning applications such as emotion and object recognition, Microsoft said.

Meanwhile, version 0.6 performance improvements include moving from a legacy “LearningPipeline” API to a new “Estimators” functionality that serves as an object that learns from data. That capability can be used to optimize the prediction capabilities of the new API. Benchmark testing revealed as much as four-figure performance improvements for use cases like accelerating the parsing of breast cancer data.

The latest release also boosts compatibility with TensorFlow by making it easier for developers to use the open source machine learning library. A new API was added to ML.Net leverages use of TensorFlow’s model scoring capabilities. Another improvement was the shift from using only “frozen” TensorFlow models to the ability to reuse those models in a “saved” model format.

The new ML.NET API “is designed to support a wider set of scenarios and closely follows [machine learning] principles and naming from other popular ML related frameworks like Apache Spark and Scikit-Learn, the company noted in a blog post.